{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import pystan, pickle\n",
    "import os\n",
    "# os.environ['STAN_NUM_THREADS'] = \"4\"\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# import networkx as nx\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "%matplotlib inline\n",
    "\n",
    "def simpleaxis(ax):\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "\n",
    "a = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_confirmed = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "url_deaths = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\"\n",
    "url_recovered = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv\"\n",
    "\n",
    "dfc = pd.read_csv(url_confirmed)\n",
    "dfd = pd.read_csv(url_deaths)\n",
    "dfr = pd.read_csv(url_recovered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = \"US_VA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# dates = dfc.columns[4:].values\n",
    "\n",
    "# # dfc2 = dfc.loc[dfc['Country/Region']==roi]\n",
    "# # dfd2 = dfd.loc[dfd['Country/Region']==roi]\n",
    "# # dfr2 = dfr.loc[dfr['Country/Region']==roi]\n",
    "\n",
    "# # print(dfc2.columns)\n",
    "\n",
    "# dfc2 = dfc.loc[dfc['Province/State']==roi]\n",
    "# dfd2 = dfd.loc[dfd['Province/State']==roi]\n",
    "# dfr2 = dfr.loc[dfr['Province/State']==roi]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# print(dfc2)\n",
    "\n",
    "\n",
    "# DF = df = pd.DataFrame(columns=['date',\n",
    "#                                 'cum_cases','cum_recover','cum_deaths',\n",
    "#                                'new_cases','new_recover','new_deaths'])\n",
    "\n",
    "\n",
    "# for i in range(len(dates)):\n",
    "# #     print(dates[i])\n",
    "#     DF.loc[i] = pd.Series({'date':dates[i],\n",
    "#                          'cum_cases':dfc2[dates[i]].values[0],# - (dfr2[dates[i]].values[0] + dfd2[dates[i]].values[0]),\n",
    "#                          'cum_recover':dfr2[dates[i]].values[0],\n",
    "#                          'cum_deaths':dfd2[dates[i]].values[0]})\n",
    "\n",
    "# DF[['new_cases', 'new_deaths', 'new_recover']] = \\\n",
    "#             DF[['cum_cases', 'cum_deaths', 'cum_recover']].diff()\n",
    "\n",
    "# # print(DF)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 '03/07/20' 0 0.0 0 0 0 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/pandas/core/series.py:1143: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return self.loc[key]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'nan'),\n",
       " Text(0, 0, '03/10/20'),\n",
       " Text(0, 0, '03/15/20'),\n",
       " Text(0, 0, '03/20/20'),\n",
       " Text(0, 0, '03/25/20'),\n",
       " Text(0, 0, '03/30/20'),\n",
       " Text(0, 0, '04/04/20'),\n",
       " Text(0, 0, '04/09/20'),\n",
       " Text(0, 0, '04/14/20'),\n",
       " Text(0, 0, '04/19/20'),\n",
       " Text(0, 0, '04/24/20')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = pd.read_csv(\"../data/covidtimeseries_\"+roi+\".csv\")\n",
    "\n",
    "# pop = {}\n",
    "# pop['Italy'] = 60500000\n",
    "# pop['United Kingdom'] = 64400000\n",
    "# pop['France'] = 66990000\n",
    "# pop['Netherlands'] = 17000000\n",
    "\n",
    "mitigate = {}\n",
    "mitigate['Italy'] = '03/9/20' #approximate date\n",
    "mitigate['Netherlands'] = '03/12/20' #approximate date\n",
    "mitigate['US_NY'] = '03/20/20'\n",
    "mitigate['UnitedKingdom'] = '3/23/20'\n",
    "\n",
    "\n",
    "# t0 := where to start time series, index space\n",
    "t0 = np.where(DF[\"new_cases\"].values>=1)[0][0] - 1 #Stan starts one day back from this \n",
    "print(DF.iloc[t0].values)\n",
    "# tm := start of mitigation, index space\n",
    "tm = t0 + 10 #np.where(mitigate[roi]==DF['dates2'])[0][0]\n",
    "\n",
    "\n",
    "#plot the data with important time stamps (t0, tm)\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('cumulatives')\n",
    "plt.plot(DF[\"cum_cases\"][t0:],'bo', label=\"cases\")\n",
    "plt.plot(DF[\"cum_recover\"][t0:],'go',label=\"recovered\")\n",
    "plt.plot(DF[\"cum_deaths\"][t0:],'ks',label=\"deaths\")\n",
    "\n",
    "plt.axvline(t0,color='k', linestyle=\"dashed\", label='t0')\n",
    "# plt.axvline(tm,color='b', linestyle=\"dashed\", label='mitigate')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('dailies')\n",
    "plt.plot(DF[\"new_cases\"][t0:],'bo', label=\"cases\")\n",
    "plt.plot(DF[\"new_recover\"][t0:],'go',label=\"recovered\")\n",
    "plt.plot(DF[\"new_deaths\"][t0:],'ks',label=\"deaths\")\n",
    "plt.axvline(t0,color='k', linestyle=\"dashed\", label='t0')\n",
    "# plt.axvline(tm,color='b', linestyle=\"dashed\", label='mitigate')\n",
    "plt.suptitle(roi)\n",
    "plt.legend()\n",
    "plt.ylim((0,200))\n",
    "ind = np.arange(0,len(DF[\"new_cases\"][t0:]),5)\n",
    "plt.xticks(ind)\n",
    "plt.gca().set_xticklabels(DF[\"dates2\"][t0:][ind], rotation=90)\n",
    "# print(DF[\"dates2\"])\n",
    "# print(\"t0 index assumed to be: day \"+str(t0))\n",
    "# print(\"t0 date: \"+DF['dates2'][t0])\n",
    "# print(\"tm index assumed to be: day \"+str(tm))\n",
    "# print(\"mitigation date: \"+DF['dates2'][tm])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF = DF.fillna(0)\n",
    "\n",
    "\n",
    "# t0 := where to start time series, index space\n",
    "t0 = np.where(DF[\"new_cases\"].values>=1)[0][0]\n",
    "\n",
    "# tm := start of mitigation, index space\n",
    "\n",
    "stan_data = {}\n",
    "stan_data['n_scale'] = 1000 #use this instead of population\n",
    "# stan_data['n_theta'] = 8\n",
    "stan_data['n_difeq'] = 5\n",
    "stan_data['n_ostates'] = 3\n",
    "stan_data['t0'] = t0-1 #to for ODE is day one, index before start of series\n",
    "stan_data['tm'] = t0 + 10\n",
    "stan_data['ts'] = np.arange(t0,90+len(DF['dates2']))\n",
    "# stan_data['y'] = (DF[['new_cases','new_recover','new_deaths']].to_numpy()).astype(int)[t0:,:]\n",
    "stan_data['n_obs'] = 90+len(DF['dates2']) - t0 \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t0 = 10\n",
    "# y = DF[\"new_cases\"].to_numpy().astype(int)[t0:]\n",
    "# plt.plot(y)\n",
    "# y = DF[\"new_cases\"][t0:]\n",
    "# plt.plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __init__ import extract_samples, make_histograms, make_lineplots, get_timing, plot_data_and_fits\n",
    "\n",
    "modelname = 'nonlinearmodel'# 'fulllinearmodel'\n",
    "modelpath = '.'\n",
    "fitpath = '/Users/aavattikutis/Documents/epidemicmodel/cccruns/fits/fit3/'\n",
    "samples = extract_samples(fitpath, modelpath, modelname, roi, 1)\n",
    "# [print(i) for i in samples.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [print(i) for i in samples.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "#setup resampler\n",
    "theta_ = ['f1','f2','sigmar','sigmad','sigmau','q','mbase','mlocation','extra_std','cbase','clocation','n_pop']\n",
    "\n",
    "# print(np.shape(samples['lambda[1,1]']))\n",
    "\n",
    "#get the nobs from scanning lambda vector\n",
    "for i in range(1000,0,-1):\n",
    "    try:\n",
    "        x = samples['lambda['+str(i)+',1]']\n",
    "        break\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "nobs = i + 1\n",
    "print(nobs)\n",
    "\n",
    "    \n",
    "clb = np.zeros((nobs))\n",
    "rlb = np.zeros((nobs))\n",
    "dlb = np.zeros((nobs))\n",
    "\n",
    "cm = np.zeros((nobs))\n",
    "rm = np.zeros((nobs))\n",
    "dm = np.zeros((nobs))\n",
    "\n",
    "cub = np.zeros((nobs))\n",
    "rub = np.zeros((nobs))\n",
    "dub = np.zeros((nobs))\n",
    "\n",
    "for i in range(1,nobs):\n",
    "    c = samples['lambda['+str(i)+',1]']\n",
    "    r = samples['lambda['+str(i)+',2]']\n",
    "    d = samples['lambda['+str(i)+',3]']\n",
    "    \n",
    "    clb[i] = np.percentile(c,2.5)\n",
    "    cub[i] = np.percentile(c,97.5)\n",
    "    cm[i] = np.percentile(c,50)\n",
    "    \n",
    "    \n",
    "    rlb[i] = np.percentile(r,2.5)\n",
    "    rub[i] = np.percentile(r,97.5) \n",
    "    rm[i] = np.percentile(r,50)\n",
    "    \n",
    "    dlb[i] = np.percentile(d,2.5)\n",
    "    dub[i] = np.percentile(d,97.5)\n",
    "    dm[i] = np.percentile(d,50)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 38)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f,ax = plt.subplots(1,1,figsize=(10,10))\n",
    "plt.title('daily counts - ' +roi+' ('+modelname+')')\n",
    "t0 = stan_data['t0'] + 1\n",
    "plt.plot(DF[\"new_cases\"].values[t0:],'bo', markeredgecolor='k',label=\"cases\")\n",
    "# plt.plot(DF[\"new_cases\"].to_numpy().astype(int)[t0:],'b*', label=\"cases\")\n",
    "plt.plot(DF[\"new_recover\"].values[t0:],'go',markeredgecolor='k',label=\"recovered\")\n",
    "plt.plot(DF[\"new_deaths\"].values[t0:],'rs',markeredgecolor='k',label=\"deaths\")\n",
    "\n",
    "indtfit = int(np.where(DF['dates2']=='04/15/20')[0] - t0)\n",
    "x = np.arange(0,indtfit)\n",
    "a=0.2\n",
    "plt.fill_between(x, clb[:indtfit], cub[:indtfit],color='b',label='95% credible C',alpha=a)\n",
    "plt.fill_between(x, rlb[:indtfit], rub[:indtfit],color='g',label='95% credible R',alpha=a)\n",
    "plt.fill_between(x, dlb[:indtfit], dub[:indtfit],color='r',label='95% credible D',alpha=a)\n",
    "\n",
    "a=1\n",
    "plt.plot(x, cm[:indtfit],color='b',label='median C',alpha=a)\n",
    "plt.plot(x, rm[:indtfit],color='g',label='median R',alpha=a)\n",
    "plt.plot(x, dm[:indtfit],color='r',label='median D',alpha=a)\n",
    "\n",
    "\n",
    "# plt.fill_between(range(nobs), clb, cub,color='b',label='95% credible C',alpha=a)\n",
    "# plt.fill_between(range(nobs), rlb, rub,color='g',label='95% credible R',alpha=a)\n",
    "# plt.fill_between(range(nobs), dlb, dub,color='r',label='95% credible D',alpha=a)\n",
    "\n",
    "\n",
    "# print(indtfit)\n",
    "plt.axvline(indtfit,label='fit before this day')\n",
    "plt.axvline(nobs-90,color='k', label='today')\n",
    "plt.legend(loc=2)\n",
    "\n",
    "# x = np.arange(0,nobs,30)+(nobs-90)\n",
    "plt.xticks(x)\n",
    "# plt.gca().set_xticklabels(x-(nobs-90))\n",
    "\n",
    "plt.xlim(0,indtfit)\n",
    "# plt.ylim((0,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile model \n",
    "# stanproj = pystan.StanModel(file=\"nonlinearmodel_predict.stan\")#, verbose=True)\n",
    "# import pickle\n",
    "# with open(\"nlproj.pkl\", \"wb\") as f:\n",
    "#     pickle.dump(stanproj, f, protocol=-1)\n",
    "\n",
    "stanproj = pickle.load(open('nlproj.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setup resampler\n",
    "theta_ = ['f1','f2','sigmar','sigmad','sigmau','q','mbase','mlocation','extra_std','cbase','clocation','n_pop']\n",
    "\n",
    "\n",
    "nobs = stan_data['n_obs']\n",
    "n = 5000\n",
    "c = np.zeros((n,nobs))\n",
    "r = np.zeros((n,nobs))\n",
    "d = np.zeros((n,nobs))\n",
    "\n",
    "ind_ = np.arange(len(samples))\n",
    "np.random.shuffle(ind_)\n",
    "k = -1\n",
    "for i in ind_[:n]:\n",
    "    k += 1\n",
    "    init_ = {}\n",
    "    for theta in theta_:\n",
    "        try:\n",
    "            init_[theta] = samples[theta][i]\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    predict = stanproj.sampling(data=stan_data,iter=1,init=[init_], chains=1, algorithm = \"Fixed_param\")\n",
    "\n",
    "    c[k,:] += predict.extract()['lambda'][0,:,0]\n",
    "    r[k,:] += predict.extract()['lambda'][0,:,1]\n",
    "    d[k,:] += predict.extract()['lambda'][0,:,2]\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "# # print(predict)\n",
    "\n",
    "# plt.plot(cub)\n",
    "# plt.plot(rub)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "clb = np.zeros((nobs))\n",
    "rlb = np.zeros((nobs))\n",
    "dlb = np.zeros((nobs))\n",
    "\n",
    "cm = np.zeros((nobs))\n",
    "rm = np.zeros((nobs))\n",
    "dm = np.zeros((nobs))\n",
    "\n",
    "cub = np.zeros((nobs))\n",
    "rub = np.zeros((nobs))\n",
    "dub = np.zeros((nobs))\n",
    "\n",
    "\n",
    "nproj = np.shape(c)[1]\n",
    "\n",
    "for i in range(nproj):\n",
    "    clb[i] = np.percentile(c[:,i],2.5)\n",
    "    cub[i] = np.percentile(c[:,i],97.5)\n",
    "    cm[i] = np.percentile(c[:,i],50)\n",
    "    \n",
    "    rlb[i] = np.percentile(r[:,i],2.5)\n",
    "    rub[i] = np.percentile(r[:,i],97.5) \n",
    "    rm[i] = np.percentile(r[:,i],50)\n",
    "    \n",
    "    dlb[i] = np.percentile(d[:,i],2.5)\n",
    "    dub[i] = np.percentile(d[:,i],97.5)\n",
    "    dm[i] = np.percentile(d[:,i],50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'US_VA (nonlinear model) \\n (pre-dash are the fitted data)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = np.where(DF['dates2']=='04/15/20')[0] - t0\n",
    "print(ind)\n",
    "f,ax = plt.subplots(1,3,figsize=(15,5))\n",
    "ax[0].plot(DF[\"new_cases\"].values[t0:],'bo', markeredgecolor='k',label=\"cases\")\n",
    "ax[0].plot(cub,'k')\n",
    "ax[0].plot(clb,'k')\n",
    "ax[0].plot(cm,color='b',label='median C',alpha=1)\n",
    "x = np.arange(nproj)\n",
    "ax[0].fill_between(x,clb, cub,color='b',label='95% credible C',alpha=0.2)\n",
    "ax[0].axvline(ind,linestyle='dashed', color='k')\n",
    "simpleaxis(ax[0])\n",
    "ax[0].legend(loc=2)\n",
    "\n",
    "ax[1].plot(DF[\"new_recover\"].values[t0:],'go', markeredgecolor='k',label=\"recovered\")\n",
    "ax[1].plot(rub,'k')\n",
    "ax[1].plot(rlb,'k')\n",
    "ax[1].plot(rm,color='g',label='median R',alpha=1)\n",
    "x = np.arange(nproj)\n",
    "ax[1].fill_between(x,rlb, rub,color='g',label='95% credible R',alpha=0.2)\n",
    "ax[1].axvline(ind,linestyle='dashed', color='k')\n",
    "simpleaxis(ax[1])\n",
    "ax[1].legend(loc=2)\n",
    "\n",
    "ax[2].plot(DF[\"new_deaths\"].values[t0:],'ko', markeredgecolor='k',label=\"deaths\")\n",
    "ax[2].plot(dub,'k')\n",
    "ax[2].plot(dlb,'k')\n",
    "ax[2].plot(dm,color='k',label='median D',alpha=1)\n",
    "x = np.arange(nproj)\n",
    "ax[2].fill_between(x,dlb, dub,color='k',label='95% credible D',alpha=0.2)\n",
    "ax[2].axvline(ind,linestyle='dashed', color='k')\n",
    "simpleaxis(ax[2])\n",
    "ax[2].legend(loc=2)\n",
    "# ax[2].set_ylim((0,1200))\n",
    "\n",
    "\n",
    "plt.suptitle(roi+' (nonlinear model) \\n (pre-dash are the fitted data)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.fill_between(range(nobs), clb, cub,color='b',label='95% credible C',alpha=a)\n",
    "# plt.fill_between(range(nobs), rlb, rub,color='g',label='95% credible R',alpha=a)\n",
    "# plt.fill_between(range(nobs), dlb, dub,color='r',label='95% credible D',alpha=a)\n",
    "\n",
    "\n",
    "# # print(indtfit)\n",
    "# plt.axvline(indtfit,label='fit before this day')\n",
    "# plt.axvline(nobs-90,color='k', label='today')\n",
    "# plt.legend(loc=2)\n",
    "\n",
    "# # x = np.arange(0,nobs,30)+(nobs-90)\n",
    "# plt.xticks(x)\n",
    "# # plt.gca().set_xticklabels(x-(nobs-90))\n",
    "\n",
    "# plt.xlim(0,indtfit)\n",
    "# # plt.ylim((0,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
